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Data normalization and clustering for big and small data and an application to clinical trials
Descriptive
TitleInfo
Title
Data normalization and clustering for big and small data and an application to clinical trials
Name
(type = personal)
NamePart
(type = family)
Zhang
NamePart
(type = given)
Yayan
NamePart
(type = date)
1986-
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Yayan Zhang
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author
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Cabrera
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Javier
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Javier Cabrera
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Advisory Committee
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chair
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Kolassa
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John E
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John E Kolassa
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Advisory Committee
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internal member
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Dicker
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Lee
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Lee Dicker
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Advisory Committee
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internal member
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Emir
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Birol
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Birol Emir
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Advisory Committee
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outside member
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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theses
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2015
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2015-05
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2015
Place
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xx
Language
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eng
Abstract
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The purpose of this thesis is to propose new methodology for data normalization and cluster prediction in order to help us unravel the structure of a data set. Such data may come from many different areas, for example clinical responses, genomic multivariate data such as microarray, educational test scores, and so on. In addition and more specifically for clinical trials this thesis proposes a new cohort size adaptive design method that will adapt cohort size eventually and finally will save time and cost while still keep the accuracy to find the target maximum tolerate dose. The new normalization method is called Fishe-Yates normalization and it has the advantage of being computationally superior than the standard quantile normalization and it improved the power of the following statistical analysis. Once the data has been normalized the observations are clustered by their pattern of response and cluster prediction is used to validate the findings. We propose a new method for cluster prediction which is a natural way to predict for hierarchical clustering. Our prediction method using nonlinear boundaries between clusters. Normalization method and clustering prediction method can help to identify subgroups of patients which has positive treatment effect. For clinical trial study, this thesis also proposes a new adaptive design which will adapt cohort size thus save time and cost to locate the target maximum tolerated dose.
Subject
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Topic
Statistics and Biostatistics
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_6324
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xiv, 105 p. : ill.)
Note
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Ph.D.
Note
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Includes bibliographical references
Subject
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Topic
Data structures (Computer science)
Subject
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Topic
Statistics
Note
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by Yayan Zhang
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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doi:10.7282/T3X068WQ
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ETD doctoral
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Rights
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The author owns the copyright to this work.
RightsHolder
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Name
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Zhang
GivenName
Yayan
Role
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Permission or license
DateTime
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2015-04-12 22:49:37
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Yayan Zhang
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Affiliation
Rutgers University. Graduate School - New Brunswick
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime
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2015-05-31
DateTime
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2016-05-30
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 30th, 2016.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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windows xp
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Statistical Profile